Defining Residential Submarkets in Planning Work

Clifford A. Lipscomb, Ph.D., discusses the importance of identifying residential submarkets, their impact on price prediction, market strategy formulation, and public policy implications. The presentation explores terminology, factors affecting neighborhoods, and a case study on Atlanta's Home Park.

• Residential
• Submarkets
• Planning
• Price Prediction
• Neighborhood

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1. Defining and Using Residential Submarkets in Planning Work Clifford A. Lipscomb, Ph.D. Director of Economic Research Greenfield Advisors, LLC Seattle, WA and Atlanta, GA United States of America

2. Greenfield Advisors 37-year-old firm headquartered in Seattle, WA, USA Real estate valuation, economic research, survey design and administration, specialty in complex valuation issues (i.e. we don t do appraisals for banks/lenders) Most of our work is litigation support Current cool project: patent infringement

3. The Big Picture People and households are heterogeneous (different) How different are they? Socioeconomic Racial Demographic Others How can we measure the differences? Are they systematic ?

4. Why is This Important? Price prediction (submarkets improve model accuracy) Formulation of market strategy Understanding housing market structure Improving lenders and investors ability to price risk associated with homeownership Public policy implications Policy tailored to one type may not be best Policy inertia (is a mid-course correction possible after a policy is implemented?)

5. Terminology What is a market? What is a submarket? How can we define submarkets? Housing stock (similar packages of housing services) Geography (traditional neighborhoods) Household characteristics (data intensive) Surveys that ask about who are your peers good for comparative studies Hybrid methods

6. Rest of the Presentation Focus on an Atlanta neighborhood Talk about external and internal factors affecting the neighborhood Discuss how a change in land use can affect the neighborhood

7. Home Park

8. Sales Price Distribution North Home Park South Home Park Less than $150K $150K 199,999 $200K 249,999 $250K 299,999 $300K + Home Park green space

9. Determining Submarkets Motivation assumptions in the literature Publically available data was limited (Census tract block group was smallest unit available) Houses are the unit of analysis, so need that level of detail in demographics Differences between renters and owners Door-to-door survey effort 51% response rate

10. Empirical Model Round 1: Cluster analysis establishes groups Round 2: Refines groups into types based on a variation of linear regression model (SUR) Houses are sorted into types based on the appraised value that minimizes error Determines the number of types without researcher pre-determination! Because what if the researcher draws an arbitrary boundary

11. Original Household Types Type A Type B Type C

12. Submarkets A: Undergraduate student renters B: Other student renters, young professionals, and young married couples C: Owners and graduate students Note: Recent research has tightened the distinctions between submarkets using different econometric estimators (Belasco, Farmer, and Lipscomb 2012)

13. Dynamic Issues What happens to neighborhood if you put in a pocket park? Simulation results Simulation 1: if preference for park access stays same Simulation 2: if preference for park is of current estimate

14. Location of New Pocket Park Type A Type B Type C

15. What Happens After Re-sorting? Models predict that mix of residents will change by 30% as a result of pocket park Student renters are forced out as new owner-occupiers enter the neighborhood Change in amenity mix = change in occupant Did pocket park simply accelerate resident mix that was going to happen anyway?

16. Types After Pocket Park Type A Type B Type C Type A Type B Type C

17. Implications Policymakers and planners need to plan with preference heterogeneity in mind A more granular level of data needed to complete comprehensive plans E.g. new MARTA rail stop will be used by what type of households? E.g. what types are attracted to TODs?

18. Implications (2) Balancing housing affordability with housing construction (micro-apartments targeting urban professionals living alone) Planners can influence the types of residents attracted to a neighborhood influence can be latent or manifest Planners plan for change and can influence that change

19. Summary Economists and planners often use data at one geographic scale when analyzing phenomena at a different scale Make sure statistical methods are an empirical translation of your theory Beware of post hoc ergo propter hoc fallacy ( after this, therefore because of this ) All methods have limitations; so seek to mitigate them and show relevance relative to other methods

20. Contact Information: Clifford A. Lipscomb, Ph.D. Director of Economic Research Greenfield Advisors, LLC 1870 The Exchange SE, Suite 100 Atlanta, GA 30339 USA E-mail: cliff@greenfieldadvisors.com Web: www.greenfieldadvisors.com Thank you